Enabling Efficient Sizing of Hybrid Power Plants: A Surrogate-Based Approach to Energy Management System Modeling

Sizing of Hybrid Power Plants (HPPs), which include wind power plants and battery energy systems, is essential to capture trade-offs among various technology mixes. To accurately represent these trade-offs, an Energy Management System (EMS) is introduced to model the operation of a battery when participating in any market, resulting in realistic operational revenues and costs. However, traditional EMS models are computationally expensive to solve, a challenge that intensifies when integrating these models into sizing processes. This research paper aims to address the critical need for a computationally efficient, accurate, and comprehensive operational model that enables quantitative assessment of HPPs. A novel methodology is introduced to approximate a state-of-the-art EMS model for HPPs involved in spot market power bidding. This approach utilizes singular value decomposition for dimension reduction and a feed-forward neural network as a regression. The accuracy of our methodology is evaluated, showing a root mean square error of 0.09 in predicting hourly operational time series. This method proves effective in accurately evaluating the operation of HPPs across various geographical locations and hence on multiple sizing problems. Furthermore, we utilized the surrogate to evaluate the profitability of several HPPs sizing, achieving a root mean square error of 0.010 on the profitability index. This shows that the developed surrogate is suitable for HPP sizing for given cost and financial assumptions.